Implementation of Fingerprint Attendance System using Matlab
A fingerprint attendance system is a device which can take attendance of people. The fingerprint attendance system is very relevant at any place since it aims at eliminating all the hassles of roll calling and malpractice and promises a full-proof as well as reliable technique of keeping records of any individual’s attendance. In the proposed system, we are using MATLAB software to compare the fingerprints of the people, whose fingerprints are already taken and stored in database. After the comparison is done, an Excel sheet is generated and the attendance of that particular person is updated, also, the result of updation is transmitted through serial port and is shown on the LCD. This sheet can be used for various purposes such as salary calculation, checking entry and exit time etc. The main advantage of using this proposed system is we can have infinite number of entries as the databases are compared and stored in a computer.
Chitresh Saraswat and Amit Kumar, “An Efficient Automatic Attendance System using Fingerprint Verification Technique”, International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 264-269.
Fakhreddine Karray, Jamil Abou Saleh, MoNours Arab and Milad Alemzadeh, “Multi Modal Biometric Systems: A State of the Art Survey,” Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Waterloo, Canada.
Anil K. Jain, Arun Ross and Salil Prabhakar, “An introduction to biometric recognition,” Circuits and Systems for Video Technology, IEEE Transactions on Volume 14, Issue 1, Jan. 2004 Page(s):4 – 20.
L. O’Gorman, “Overview of fingerprint verification technologies,” Elsevier Information Security Technical Report, Vol. 3, No. 1, 1998.
Brightness Preserving Histogram Equalization with Maximum Entropy: A Variational Perspective Chao Wang and Zhongfu Ye, IEEE Transactions on Consumer Electronics, Vol. 51, No. 4, NOVEMBER 2005.
B.G. Sherlock. D.M. Monro. K. Millard., “Fingerprint enhancement by directional Fourier filtering,” IEE hoc.-Vis. Image Signal Processing, Vol. 141, No. 2, April 1994.
A New Local Adaptive Thresholding Technique in Binarization, T. Romen Singh, Sudipta Roy, O. Imocha Singh, Tejmani Sinam, Kh.Manglem Singh, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 2, November 2011.
Asker M. Bazen and Sabih H. Gerez, “Segmentation of Fingerprint Images,” Workshop on Circuits, Systems and Signal Processing, Veldhoven, 2001.
E. Hastings, “A Survey of Thinning Methodologies,” Pattern analysis and Machine Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-885, 1992.
U. Halici, L. C. Jain, A. Erol, "Introduction to Fingerprint Recognition," Intelligent Biometric Techniques in Fingerprint and Face Recognition, L.C. Jain, U. Halici, I.Hayashi, S.B. Lee, S. Tsutsui (editors), CRC Press, 1999.
Manvjeet Kaur, Mukhwinder Singh, Akshay Girdhar, and Parvinder S. Sandhu, “Fingerprint Verification System using Minutiae Extraction Technique,” World Academy of Science, Engineering and Technology 46 2008.
Ying Jie, YuanYi fang, Zhang Renjie, Song Qifa. ”Fingerprint minutiae matching algorithm for real time system.” Pattern Recognition 39 (2006) 143 – 146.
Feng Zhao, Xiaoou Tang, “Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction,” Pattern Recognition 40(4): 1270-1281 (2007).
Pradeep M. Patil, Shekar R. Suralkar and Faiyaz B. Sheikh, “Rotation Invariant Thinning Algorithm to Detect Ridge Bifurcations for Fingerprint Identification”, IEEE International Conference on Tools with Artificial Intelligence, 2005.
Cheong Hee Park and Haesun Park, “Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis,” Pattern Recognition 38 (2005) 495 – 503.
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